Having evolved rapidly and steeply in the past two decades, the wireless communication systems now offer a wide variety of services such as multimedia communications, web browsing, audio and video streaming, online game playing, etc. Not surprisingly, the number of users accessing these services has also increased drastically. The resulting increase in data traffic together with the scarceness of wireless spectrum resources has made high efficiency data transmission an essential factor in the design of wireless communication systems.
The use of multiple-input-multiple-output (MIMO) techniques has thus become the new frontier of wireless communications. The MIMO technique basically employs multiple antennas at both transmitter and receiver to allow the transmission of parallel data streams over available spatial channels, which in turn makes high-rate data transfers and improved link quality possible.
The optimal MIMO detector for a wireless communication system is a maximum-likelihood (ML) detector, which seeks to minimize the average probability of error between the detected symbols and the transmitted symbols. However, designing an ML detector is equivalent to solving a non-deterministic polynomial-time (NP)-hard problem, thus impractical to implement due to its exponential complexity.
In practice, people have proposed different linear implementations to approximate ML detectors so that the computational complexity is manageable. Examples of these approximation implementations include the minimum mean square error (MMSE) detectors and the zero forcing (ZF) detectors, which have conventionally been used for their low complexity. However, the performance of these conventional linear detectors drops significantly in poor channel conditions. Thus, there remains a considerable need for methods and apparatus in low-complexity MIMO detector designs that have high performance even in poor channel conditions.